NiyānData Version Control

A self-hosted data forge built on Git, Git LFS, and S3-compatible storage for researchers and machine-learning teams.

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A versioned dataset flowing through branches into object storage

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Git-based

Every dataset is an ordinary Git repository. Branches, tags, commits, and familiar tooling remain available.

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S3 integration

Upload, download, and stream data directly to and from S3-compatible storage for faster transfers and a smoother experience.

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Built for research

Keep datasets reproducible alongside the code that consumes them, from an individual workstation to a shared lab installation.

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Python Integration

Stream remote files into Python workflows or download them explicitly without checking out an entire dataset.

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Self-hosted

Own the control plane, Git repositories, metadata, and object storage. Deploy the complete stack with Docker Compose.

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Open by design

Standard Git and Git LFS remain supported, while documented interfaces make datasets easier to access.

A familiar workflow for data

Niyān adds the coordination that large datasets need without hiding the tools underneath. Use the recommended CLI for the smoothest experience, or work with standard Git and Git LFS if you prefer.

$ pipx install niyan
$ niyan auth login https://data.example.org
$ niyan dataset clone lab/cell-images
$ cd cell-images
$ niyan status

Reliable infrastructure for serious data

Niyān is built on top of a few reliable components: Git records history, Git LFS represents large files, S3-compatible storage holds their contents, and Niyān's custom control plane manages identity, access, and the web experience.

One source of truth

Pin an immutable dataset commit beside your research code and return to exactly the same inputs later.

No proprietary repository format

Clone, inspect, and recover datasets with established tools, even when the Niyān CLI is not available.

Designed to be operated

A focused control plane and a small number of durable services keep institutional deployment straightforward.